TY - GEN
T1 - Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma
T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
AU - P. P. Abdul Majeed, Anwar
AU - Mohd Isa, Wan Hasbullah
AU - Abdul Rauf, Ahmad Ridhauddin
AU - Ahmad, Ahmad Fakhri
AU - Arzmi, Mohd Hafiz
AU - Hafizh, Hadyan
AU - Yap, Eng Hwa
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.
AB - Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis.
KW - Computer-aided diagnosis
KW - Deep learning
KW - Feature-based transfer learning
KW - Machine learning
KW - Oral cancer
KW - Oral squamous cell carcinoma
UR - http://www.scopus.com/inward/record.url?scp=85187801208&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8498-5_34
DO - 10.1007/978-981-99-8498-5_34
M3 - Conference Proceeding
AN - SCOPUS:85187801208
SN - 9789819984978
T3 - Lecture Notes in Networks and Systems
SP - 433
EP - 438
BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
A2 - Tan, Andrew
A2 - Zhu, Fan
A2 - Jiang, Haochuan
A2 - Mostafa, Kazi
A2 - Yap, Eng Hwa
A2 - Chen, Leo
A2 - Olule, Lillian J. A.
A2 - Myung, Hyun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2023 through 23 August 2023
ER -
